1. Field of the Invention
The present invention relates generally to apparatus and methods for processing and/or representing sensor data, such as mechanical or medical sensor data.
2. Discussion of the Related Art
Mechanical devices and biomedical monitoring devices such as pulse oximeters, glucose sensors, electrocardiograms, capnometers, fetal monitors, electromyograms, electroencephalograms, and ultrasounds are sensitive to noise and artifacts. Typical sources of noise and artifacts include baseline wander, electrode-motion artifacts, physiological artifacts, high-frequency noise, and external interference. Some artifacts can resemble real processes, such as ectopic beats, and cannot be removed reliably by simple filters; however, these are removable by the techniques taught herein. In addition, mechanical devices and biomedical monitoring devices address a limited number of parameters. It would be desirable to expand the number of parameters measured, such as to additional biomedical state parameters.
Patents related to the current invention are summarized herein.
Mechanical Systems
Several reports of diagnostics and prognostics applied to mechanical systems have been reported.
Vibrational Analysis
R. Klein “Method and System for Diagnostics and Prognostics of a Mechanical System”, U.S. Pat. No. 7,027,953 B2 (Apr. 11, 2006) describes a vibrational analysis system for diagnosis of health of a mechanical system by reference to vibration signature data from multiple domains, which aggregates several features applicable to a desired fault for trend analysis of the health of the mechanical system.
Intelligent System
S. Patel, et. al. “Process and System for Developing Predictive Diagnostic Algorithms in a Machine”, U.S. Pat. No. 6,405,108 B1 (Jun. 11, 2002) describe a process for developing an algorithm for predicting failures in a system, such as a locomotive, comprising conducting a failure mode analysis to identify a subsystem, collecting expert data on the subsystem, and generating a predicting signal for identifying failure modes, where the system uses external variables that affect the predictive accuracy of the system.
C. Bjornson, “Apparatus and Method for Monitoring and Maintaining Plant Equipment”, U.S. Pat. No. 6,505,145 B1 (Jan. 11, 2003) describes a computer system that implements a process for gathering, synthesizing, and analyzing data related to a pump and/or a seal, in which data are gathered, the data is synthesized and analyzed, a root cause is determined, and the system suggests a corrective action.
C. Bjornson, “Apparatus and Method for Monitoring and Maintaining Plant Equipment”, U.S. Pat. No. 6,728,660 B2 (Apr. 27, 2004) describes a computer system that implements a process for gathering, synthesizing, and analyzing data related to a pump and/or a seal, in which data are gathered, the data is synthesized and analyzed, and a root cause is determined to allow a non-specialist to properly identify and diagnose a failure associated with a mechanical seal and pump.
K. Pattipatti, et. al. “Intelligent Model-Based Diagnostics for System Monitoring, Diagnosis and Maintenance”, U.S. Pat. No. 7,536,277 B2 (May 19, 2009) and K. Pattipatti, et. al. “Intelligent Model-Based Diagnostics for System Monitoring, Diagnosis and Maintenance”, U.S. Pat. No. 7,260,501 B2 (Aug. 21, 2007) both describe systems and methods for monitoring, diagnosing, and for condition-based maintenance of a mechanical system, where model-based diagnostic methodologies combine or integrate analytical models and graph-based dependency models to enhance diagnostic performance.
Inferred Data
R. Tryon, et. al. “Method and Apparatus for Predicting Failure in a System”, U.S. Pat. No. 7,006,947 B2 (Feb. 28, 2006) describe a method and apparatus for predicting system failure or reliability using a computer implemented model relying on probabilistic analysis, where the model uses data obtained from references and data inferred from acquired data. More specifically, the method and apparatus uses a pre-selected probabilistic model operating on a specific load to the system while the system is under operation.
Virtual Prototyping
R. Tryon, et. al. “Method and Apparatus for Predicting Failure of a Component”, U.S. Pat. No. 7,016,825 B1 (Mar. 21, 2006) describe a method and apparatus for predicting component failure using a probabilistic model of a material's microstructural-based response to fatigue using virtual prototyping, where the virtual prototyping simulates grain size, grain orientation, and micro-applied stress in fatigue of the component.
R. Tryon, et. al. “Method and Apparatus for Predicting Failure of a Component, and for Determining a Grain Orientation Factor for a Material”, U.S. Pat. No. 7,480,601 B2 (Jan. 20, 2009) describe a method and apparatus for predicting component failure using a probabilistic model of a material's microstructural-based response to fatigue using a computer simulation of multiple incarnations of real material behavior or virtual prototyping.
Medical Systems
Several reports of systems applied to biomedical systems have been reported.
Lung Volume
M. Sackner, et. al. “Systems and Methods for Respiratory Event Detection”, U.S. patent application no. 2008/0082018 A1 (Apr. 3, 2008) describe a system and method of processing respiratory signals from inductive plethysmographic sensors in an ambulatory setting that filters for artifact rejection to improve calibration of sensor data and to produce output indicative of lung volume.
Pulse Oximeter
J. Scharf, et. al. “Separating Motion from Cardiac Signals Using Second Order Derivative of the Photo-Plethysmograph and Fast Fourier Transforms”, U.S. Pat. No. 7,020,507 B2 (Mar. 28, 2006) describes the use of filtering photo-plethysmograph data in the time domain to remove motion artifacts.
M. Diab, et. al. “Plethysmograph Pulse Recognition Processor”, U.S. Pat. No. 6,463,311 B1 (Oct. 8, 2002) describe an intelligent, rule-based processor for recognition of individual pulses in a pulse oximeter-derived photo-plethysmograph waveform operating using a first phase to detect candidate pulses and a second phase applying a plethysmograph model to the candidate pulses resulting in period and signal strength of each pulse along with pulse density.
C. Baker, et. al. “Method and Apparatus for Estimating Physiological Parameters Using Model-Based Adaptive Filtering”, U.S. Pat. No. 5,853,364 (Dec. 29, 1998) describe a method and apparatus for processing pulse oximeter data taking into account physical limitations using mathematical models to estimate physiological parameters.
Cardiac
J. McNames, et. al. “Method, System, and Apparatus for Cardiovascular Signal Analysis, Modeling, and Monitoring”, U.S. patent application publication no. 2009/0069647 A1 (Mar. 12, 2009) describe a method and apparatus to monitor arterial blood pressure, pulse oximetry, and intracranial pressure to yield heart rate, respiratory rate, and pulse pressure variation using a statistical state-space model of cardiovascular signals and a generalized Kalman filter to simultaneously estimate and track the cardiovascular parameters of interest.
M. Sackner, et. al. “Method and System for Extracting Cardiac Parameters from Plethysmograph Signals”, U.S. patent application publication no. 2008/0027341 A1 (Jan. 31, 2008) describe a method and system for extracting cardiac parameters from ambulatory plethysmographic signal to determine ventricular wall motion.
Hemorrhage
P. Cox, et. al. “Methods and Systems for Non-Invasive Internal Hemorrhage Detection”, International Publication no. WO 2008/055173 A2 (May 8, 2008) describe a method and system for detecting internal hemorrhaging using a probabilistic network operating on data from an electrocardiogram, a photoplethysmogram, and oxygen, respiratory, skin temperature, and blood pressure measurements to determine if the person has internal hemorrhaging.
Disease Detection
V. Karlov, et. al. “Diagnosing Inapparent Diseases From Common Clinical Tests Using Bayesian Analysis”, U.S. patent application publication no. 2009/0024332 A1 (Jan. 22, 2009) describe a system and method of diagnosing or screening for diseases using a Bayesian probability estimation technique on a database of clinical data.